PLSC 201 Lecture Notes - Zahi Hawass, Giza Plateau, Egyptology

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holdengames19

26 May 2023

School

Winthrop University

Department

Political Science

Course

PLSC 201

Professor

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Document Summary

Introduction: zahi hawass is a renowned archaeologist who has made significant contributions to the field of egyptology. He was born in damietta, egypt, in 1947 and completed his phd in egyptology at the. Hawass has led numerous excavations and served as the secretary- General of the supreme council of antiquities in egypt from 2002 to 2011. In this essay, we will explore the life and work of zahi hawass, including his contributions to the discovery of the osiris shaft, his role in the management of archaeological sites in egypt, and his criticisms. Body 1: one of zahi hawass"s most significant contributions to archaeology was his work on the osiris. The tomb structure, located beneath the pyramid of khafre on the giza plateau, was excavated by. The discovery of the tomb provided insight into the usage of the giza necropolis throughout pharaonic history and demonstrated the reuse of an old kingdom cenotaph in the saite.

Related Questions

I need to write a full written report for the device that I have been working on such as “SPECIAL BLOOD PRESSURE MACHINE PRODUCT THAT IS A WIRELESS, REMOTE MONITORING DEVICE THAT CONNECTS TO A SMARTPHONE APP WITH ACCURATE MEASUREMENTS”. The final report should not exceed 15 pages including drawings and references. The report should be organized as a written version of the marketing pitch that generally includes the following :

I need to include the information outlined in table 6.2.3 above in the picture where you can integrate it into the R&D section of the report. You can describe the milestones achieved in each stage of the development timeline and any challenges faced during each phase.

Remember to keep the report concise and easy to read. Use bullet points, charts, and graphics to enhance the presentation of your information. Good luck with your report!

1 Cover slide: The cover slide should include the product name, your company's logo, and any other relevant information.

2 Elevator statement: Provide a brief summary of the product, including its unique selling proposition, target audience, and benefits.

3 The clinical need: Describe the clinical need for the product and how it solves an existing problem.

4 The solution: Provide a detailed explanation of the product, including its features, benefits, and how it works.

5 The market: Describe the target market for the product and the potential size of the market.

6 Intellectual property (IP): Discuss any patents, trademarks, or copyrights related to the product.

7 R&D: Describe the research and development process for the product, including any challenges faced and how they were overcome.

8 Regulatory strategy: Outline the regulatory requirements for the product and how you plan to comply with them.

9 Clinical studies: Describe any clinical studies conducted on the product and their results.

10 Reimbursement: Explain how the product will be reimbursed and any relevant insurance coverage.

11 Business model: Describe the business model for the product, including pricing, distribution, and revenue streams.

12 Marketing and sales: Explain the marketing and sales strategy for the product, including how you plan to reach your target audience.

13 Financial information: Provide financial projections for the product, including revenue, expenses, and profit margins.

14 Exit scenarios: Discuss potential exit scenarios for the product, such as acquisition or IPO.

15 The ask: Outline any funding needs for the product and how the funds will be used.

16 Management team: Introduce the management team and their relevant experience.

17 Closing slide: Include a closing slide that summarizes the key points of the report.

18 Appendix: Include any additional information that supports the report, such as technical specifications, drawings, or references.

rafi_mokdad

1 Introduction

Your goal is to formulate a set of psycholinguistic hypotheses that interest you and use previously collected data to investigate these hypotheses. Your hypothesis should be plausible-ish, but feel free to be creative in what you investigate. Any statistical methods are ok to use in your analysis. It’s not required that you have statistically significant results, but it is a requirement to do the analyses properly.

GIVE R CODES TO RUN

What I expect: To be written up in the form of a lab report that discusses your findings.

It should include an introduction, hypotheses, methods (what data was used, where it came from, what statistical tests were chosen and why, etc), results, discussion (what do the results mean scientifically), and a conclusion.
The number of tests to use, doing at least two analyses (complete with checks for assumptions).
You should make sure to use tables and plots when appropriate. And test assumptions!
Remember to motivate your choices and adequately illustrate your findings using plots and graphs.

2 Working with the English Lexicon Project

The English Lexicon Project (ELP) is a corpus of English lexical items with behavioral data for said items. That is, it contains accuracy and reaction times in lexical decision experiments and naming latencies in picture naming experiments. In addition to these latencies, you are also provided with the standard deviations for each word for each experiment-type, as well as the number of observations for each item. Basically, they’ve already run the experiments for you, so you just need to analyze them.

The data frame for the ELP is massive. It has 12704 observations and 24 variables. But, it’s unlikely that you’ll be using that much data. Instead, the observations and variables you use will be determined by your experimental hypothesis and the particular analysis you select.

Reading in a .csv file is slightly different than a .txt file, since it’s separated by commas and not tabs. You can use the following (remember to change file.choose() if you are on a system that uses a different command to bring up the user interface):

data <- read.csv(file.choose(), header=T)

Type head(data) to ensure you correctly loaded the file. And save your work often!

2.1 Explanation of the columns in the ELP dataset

There are a lot of columns in the ELP dataset. Here is a brief description of each column, and full descriptions can be found in the information on the ELP website.

2.1.1 Independent variables

Column Explanation

Word The specific lexical item

Length Number of orthographic characters (letters) composing the word Freq_Hal Word frequency

Ortho_N Number of orthographic neighbours (e.g., ace = ate, act, ale, are, …) Phono_N Number of phonological neighbours (e.g., wings = wins, rings, …) Phono_N_H Number of phonological neighbours including homophones Freq_N Average frequency of an orthographic neighbourhood of a particular word

Freq_N_P Average frequency of the phonological neighbourhood of a particular word

BG_Sum Sum of the bigram count for a particular word

BG_Mean Average bigram count for a particular word

BG_Freq_By_Pos Sum of the bigram count (by position) for a particular word NPhon Number of phonemes in the standard pronunciation for a particular word2

NSyll Number of syllables in the standard pronunciation for a particular word

MorphSp Morphological (orthographic) composition of a particular word. See for a key to the symbols.

MorphPr Morphological (phonetic) composition of a particular word. See for a key to the symbols.

NMorph Number of morphemes

2.1.2 Dependent variables

Lexical decision task

Column Explanation

I_Mean_RT Mean reaction time for a particular word (in ms) across all participants

I_SD Standard deviation of mean reaction time for a particular word (in ms) across all participants

Obs Number of observations comprising reaction time data for a particular word

I_Mean_Accuracy Average accuracy for a particular word across all participants

Naming task

Column Explanation

I_NMG_Mean_RT Mean naming latency for a particular word (in ms) across all participants

I_NMG_SD Standard deviation of mean naming latency for a particular word (in ms) across all participants

I_NMG_Obs Number of observations comprising naming latency data for a particular word

I_NMG_Mean_Accuracy Average accuracy for a particular word across all participants

4 Hints

You will probably want to do some serious subsetting of the data. Isolate just the rows and columns you want. Decide if you want to bin your lexical variables, e.g., create nominal categories such as high vs low frequency words, or use continuous measures, e.g., correlate your dependent variable against your independent variables. In the LADEC data, there are also rows with missing data (what R calls NA). Since many statistical tests prefer to have groups of equal sizes, you might need to remove rows with missing data. Do this using the na.omit() function, but only do this after you have selected which rows you’ll be working with. For instance, if the data was stored in d, and you only wanted columns 1, 2, 3, 40, 41, and 42, you can do

d2 <- d[,c(1,2,3,40,41,42)]

d2 <- na.omit(d2)

to save just those columns to d2, and then strip out the rows with NA values. One way to get started is to pick a column from a dataset and think about whether there are groups you could subset out, based on data from other columns. Or, you can take that column and think about how it might correlate with another column in the dataset. These are only suggestions, though. There’s a lot of data here, and lots of things one could look at. You don’t need statistically significant results to write the paper, so you can be creative in what you test.

Feel free to search the primary literature for inspiration. If the methods others use are too tricky, do not let yourself get stuck on one paper. For any particular topic, there are almost always many papers out there once you discover the hidden keywords to use (they’re most often in the introduction or background, and in the discussion).

Pick a question and remember to follow the steps before proceeding with the actual statistical tests. Remember to report your statistics in the appropriate manner, including for your tests for assumptions and for the main test you’re running. Consult the Gries book for some basic forms you can borrow from if you need help with the style for reporting results.

Other help: For help with plotting, use Quick-R ( Or, if you’re more adventurous, you could try ggplot2 ( which makes prettier plots than the standard R plotting tools.

astray

Edward Witten - How Do Scientific Breakthroughs Happen? Closer To Truth

"Asymptotic freedom," the last big breakthrough, was discovered by Gross, Wilcheck and Pulitzer just before I started my graduate school. It took me a year to understand what was happening in the world of physics. The standard model involved new mathematics unfamiliar to both physicists and mathematicians at the time, and whose exploration proved to be very rich.

The standard model is based on a nonlinear version of Maxwell's equations, where two waves interact and scatter each other but cannot be treated classically the way light waves can be treated, so it has to be treated quantum mechanically. In 1976, right after I finished my graduate school, there was a breakthrough in the math of nonbillion gauge theory applied to these strong interactions.

In 1982 and 1983, John Schwartz, Michael Green and Lars Brink made enough progress in string theory that I started to take their work seriously, though I knew it was a long-term proposition. In 1984, a colleague who had just returned from Aspen mentioned the development that had been achieved, and at that instant, I knew I would be working on string theories because it was like magic.

The breakthrough of Green and Schwartz in August 1984 made the difficulties in elementary particle physics go away. It had a huge impact on me, and I was sure that my interest would never be the same again. By the mid-1980s, I had caught up to where others were. As a graduate student, I was obsessed with the fact that the equations of the Standard Model are challenging to solve because the quantum effects are significant.

In 1994, Cyborg and I collaborated on an interesting instance of what people in the physics and mathematics fields call "Cyborg written theory." The equations of the Standard Model are tough to solve because quantum effects are large, and it proved to be particularly captivating.

The story that I presented in a lecture at USC (University of Southern California) in 1995, where I sort of unified the string theories, really resulted from an attempt to disprove some of them. In 1995, I was successful in doing something similar for string theory. I tried to reduce the number of string theories by showing that some were inconsistent.

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PLSC 201 Lecture Notes - Zahi Hawass, Giza Plateau, Egyptology

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